Methods and systems for undetermined query analytics

ABSTRACT

Set analysis may be used to determine the best data analysis model(s) (e.g., data charts, data tables, data graphs, data maps, graphical objects, key performance indicators (KPIs), etc.) for representing the results of an undetermined query (e.g., an imprecise query, an undefined query, an incomplete query, a partially expressed query, a portioned query, etc.).

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Provided are methods and systemsfor undetermined query analytics.

The methods described may include receiving undetermined queryinformation, determining, based on the undetermined query information,one or more data constraints and one or more data analysis models,determining, based on the one or more data constraints, an aggregateddataset, determining, based on the aggregated dataset and the one ormore data analysis models, an optimal data analysis model, and causingan output of the optimal data analysis model. The optimal data analysismodel may indicate at least a portion of the aggregated dataset.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a schematic diagram showing an embodiment of a system formingan implementation of the disclosed methods;

FIG. 2 is a set of tables showing exemplary Tables 1-5 of a simpledatabase and associations between variables in the tables;

FIG. 3 is a schematic flowchart showing basic steps performed whenextracting information from a database;

FIG. 4 illustrates an example global symbol service;

FIG. 5A is a schematic diagram showing how an undetermined queryoperates on a scope to generate a data subset;

FIG. 5B is an overview of the relations between data model, indexes indisk and windowed view of disk indexes in memory;

FIG. 5C illustrates an example application of bidirectional tableindexes and bidirectional association indexes;

FIG. 5D is a representation of the data structure used for indexlets;

FIG. 5E illustrates an example of inter-table inferencing usingindexlets;

FIG. 5F illustrates an example of linking indexlets of different tables;

FIGS. 5G-I illustrate example data analysis models;

FIG. 6 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received afterprocessing by an external engine;

FIG. 7 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 6 ;

FIG. 8 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after secondcomputations from an external engine;

FIG. 9 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 8 ;

FIG. 10 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after thirdcomputations from an external engine;

FIG. 11 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 10 ;

FIG. 12 is a table showing results from computations based on differentselections in the presentation of FIG. 10 ;

FIG. 13 is a schematic graphical presentation showing a further set ofselections and a diagram of data associated to the selections asreceived after third computations from an external engine;

FIG. 14 is a flow chart illustrating an example method; and

FIG. 15 is an exemplary operating environment for performing thedisclosed methods.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described inmore detail, it is to be understood that the methods and systems are notlimited to specific steps, processes, components, or structuredescribed, or to the order or particular combination of such steps orcomponents as described. It is also to be understood that theterminology used herein is for the purpose of describing exemplaryembodiments only and is not intended to be restrictive or limiting.

As used herein the singular forms “a,” “an,” and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise. Values expressed as approximations, by use of antecedentssuch as “about” or “approximately,” shall include reasonable variationsfrom the referenced values. If such approximate values are included withranges, not only are the endpoints considered approximations, themagnitude of the range shall also be considered an approximation. Listsare to be considered exemplary and not restricted or limited to theelements comprising the list or to the order in which the elements havebeen listed unless the context clearly dictates otherwise.

Throughout the specification and claims of this disclosure, thefollowing words have the meaning that is set forth: “comprise” andvariations of the word, such as “comprising” and “comprises,” meanincluding but not limited to, and are not intended to exclude, forexample, other additives, components, integers or steps. “Exemplary”means “an example of”, but not essential, necessary, or restricted orlimited to, nor does it convey an indication of a preferred or idealembodiment. “Include” and variations of the word, such as “including”are not intended to mean something that is restricted or limited to whatis indicated as being included, or to exclude what is not indicated.“May” means something that is permissive but not restrictive orlimiting. “Optional” or “optionally” means something that may or may notbe included without changing the result or what is being described.“Prefer” and variations of the word such as “preferred” or “preferably”mean something that is exemplary and more ideal, but not required. “Suchas” means something that is exemplary.

Steps and components described herein as being used to perform thedisclosed methods and construct the disclosed systems are exemplaryunless the context clearly dictates otherwise. It is to be understoodthat when combinations, subsets, interactions, groups, etc. of thesesteps and components are disclosed, that while specific reference ofeach various individual and collective combinations and permutation ofthese may not be explicitly disclosed, each is specifically contemplatedand described herein, for all methods and systems. This applies to allaspects of this application including, but not limited to, steps indisclosed methods and/or the components disclosed in the systems. Thus,if there are a variety of additional steps that can be performed orcomponents that can be added, it is understood that each of theseadditional steps can be performed and components added with any specificembodiment or combination of embodiments of the disclosed systems andmethods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the Examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices,whether internal, networked, or cloud-based.

Embodiments of the methods and systems are described below withreference to diagrams, flowcharts, and other illustrations of methods,systems, apparatuses, and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general-purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present disclosure relates to computer-implemented methods andsystems for undetermined query analytics. A computing device (e.g., acomputer, a cloud-based device, a server, a smart device, etc.) mayreceive, for example, via a user interface, a query. The query may be,for example, an undetermined query (e.g., an imprecise query, anundefined query, an incomplete query, a partially expressed query, aportioned query, etc.). The computing device may be configured toanalyze the query based on aggregation functions that are qualified tooperate on a subset of data records (e.g., rather than a currentselection of data records and/or a total selection of data records,etc.).

The computing device may perform set analysis (e.g., set expressionanalysis, etc.) on any undetermined query to determine and/or define anaggregation set. To define an aggregation set for an undetermined query,the computing device may consider and/or account for items (e.g.,compositional elements, predicates, etc.), constraints (e.g., dataconstraints, logical constraints, etc.) of the query, and one or moredata analysis models (e.g., data charts, data tables, data graphs, datamaps, graphical objects, key performance indicators (KPIs), etc.). Forexample, the computing device may determine how each input item and/orcomputational element fits a data analysis model based on the dataanalysis model's capacity and/or projectability of an item (e.g.,whether it has any condition, whether the condition results on one ormultiple values, etc.). For example, the computing device may determinean optimal data analysis model from one or more data analysis modelsdetermined from an undetermined query that best fits each input itemand/or computational element.

For example, the computing device may receive each of the followingundetermined business-related queries:

-   -   Query 1: Sales by product where sales>2000    -   Query 2: Products with sales>2000    -   Query 3: Number of products with sales>2000

Query 1, Query 2, and Query 3, each include similar (e.g., conceptuallysimilar, etc.) items (e.g., compositional elements, predicates, etc.),such as “sales,” “products,” “>2000,” and/or the like. The computingdevice may, for example, use natural language parsing and/or metadataanalysis to determine the items and/or any constraints of the query,such as a default analysis period, a required data/element selection,and/or the like. The computing device may determine/perform a differentset analysis for Query 1, Query 2, Query 3, and/or any otherundetermined query based on, for example, an order/arrangement of items(and/or constraints) of the query and/or the composition (e.g.,dimensions, measures, etc.) of one or more data analysis models (e.g.,data charts, data tables, data graphs, data maps, graphical objects, keyperformance indicators (KPIs), etc.). The computing device maydetermine/perform a different set analysis for Query 1, Query 2, andQuery 3 (and/or any other query) according to novel algorithms describedherein.

FIG. 1 shows an example associative data indexing engine 100. Theassociative data indexing engine 100 may determine and/or generate aresponse to a query. The query may be, for example, an undeterminedquery (e.g., an imprecise query, an undefined query, an incompletequery, a partially expressed query, a portioned query, etc.). Theassociative data indexing engine 100 may analyze the query based on oneor more novel aggregation functions, for example, aggregation functionsthat are qualified to operate on a subset of data records (e.g., ratherthan a current selection of data records and/or a total selection ofdata records, etc.) and output a response. The response may be, forexample, a visualization and/or one or more data analysis models (e.g.,data charts, data tables, data graphs, data maps, graphical objects, keyperformance indicators (KPIs), etc.) that best fit aggregated dataassociated with the query.

FIG. 1 shows the associative data indexing engine 100 with data flowingin from the left and operations starting from a script engine 104 andgoing clockwise (indicated by the clockwise arrow) to export features118. Data from a data source 102 can be extracted by a script engine104. The data source 102 can comprise any type of known database and/ordata store, such as relational databases, post-relational databases,object-oriented databases, hierarchical databases, flat files,spreadsheets, etc. The Internet may also be regarded as a database inthe context of the present disclosure. A visual interface can be used asan alternative or combined with a script engine 104. The script engine104 can read record by record from the data source 102 and data can bestored or appended to symbol and data tables in an internal database120. Read data can be referred to as a data set.

In an aspect, the extraction of the data can comprise extracting aninitial data set or scope from the data source 102, e.g. by reading theinitial data set into the primary memory (e.g. RAM) of the computer. Theinitial data set can comprise the entire contents of the data source102, or a subset thereof. The internal database 120 can comprise theextracted data and symbol tables. Symbol tables can be created for eachfield and, in one aspect, can only contain the distinct field values,each of which can be represented by their clear text meaning and a bitfilled pointer. The data tables can contain said bit filled pointers.

In the case of a query, such as an undetermined query (e.g., animprecise query, an undefined query, an incomplete query, a partiallyexpressed query, a portioned query, etc.), of the data source 102, ascope can be defined by the tables included in a SELECT statement (orequivalent) and how these are joined. In an aspect, the SELECT statementcan be SQL (Structured Query Language) based. For an Internet search,the scope can be an index of found web pages, for example, organized asone or more tables. A result of scope definition can be a data set.

Once the data has been extracted, a user interface can be generated tofacilitate dynamic display of the data. By way of example, a particularview of a particular dataset or data subset generated for a user can bereferred to as a state space or a session. The methods and systems candynamically generate one or more visual representations of the data topresent in the state space.

A user can make a selection in the data set, causing a logical inferenceengine 106 to evaluate a number of filters on the data set. For example,a query on a database that holds data of placed orders, could berequesting results matching an order year of ‘1999’ and a client groupbe ‘Nisse.’ The selection may thus be uniquely defined by a list ofincluded fields and, for each field, a list of selected values or, moregenerally, a condition. Based on the selection, the logical inferenceengine 106 can generate a data subset that represents a part of thescope. The data subset may thus contain a set of relevant data recordsfrom the scope, or a list of references (e.g. indices, pointers, orbinary numbers) to these relevant data records. The logical inferenceengine 106 can process the selection and can determine what otherselections are possible based on the current selections. In an aspect,flags can enable the logical inference engine 106 to work out thepossible selections. By way of example, two flags can be used: the firstflag can represent whether a value is selected or not, the second canrepresent whether or not a value selection is possible. For every clickin an application, states and colors for all field values can becalculated. These can be referred to as state vectors, which can allowfor state evaluation propagation between tables.

The logical inference engine 106 can utilize an associative model toconnect data. In the associative model, all the fields in the data modelhave a logical association with every other field in the data model. Anexample, data model 501 is shown in FIG. 5B. The data model 501illustrates connections between a plurality of tables that representlogical associations. Depending on the amount of data, the data model501 can be too large to be loaded into memory. To address this issue,the logical inference engine 106 can generate one or more indexes forthe data model. The one or more indexes can be loaded into memoryinstead of the data model 501. The one or more indexes can be used asthe associative model. An index is used by database management programsto provide quick and efficient associative access to a table's records.An index is a data structure (for example, a B-tree, a hash table, andthe like) that stores attributes (e.g., values) for a specific column ina table. A B-tree is a self-balancing tree data structure that keepsdata sorted and allows searches, sequential access, insertions, anddeletions in logarithmic time. The B-tree is a generalization of abinary search tree in that a node can have more than two children. Ahash table (also referred to as a hash index) can comprise a collectionof buckets organized in an array. A hash function maps index keys tocorresponding buckets in the hash index.

An example database, as shown in FIG. 2 , can comprise a number of datatables (Tables 1-5). Each data table can contain data values of a numberof data variables. For example, in Table 1 each data record containsdata values of the data variables “Product,” “Price,” and “Part.” Ifthere is no specific value in a field of the data record, this field isconsidered to hold a NULL-value. Similarly, in Table 2 each data recordcontains values of the variables “Date,” “Client,” “Product,” and“Number.” In Table 3 each data record contains values of variable “Date”as “Year,” “Month” and “Day.” In Table 4 each data record containsvalues of variables “Client” and “Country,” and in Table 5 each datarecord contains values of variables “Country,” “Capital,” and“Population.” Typically, the data values are stored in the form ofASCII-coded strings, but can be stored in any form.

Queries that compare for equality to a string can retrieve values veryfast using a hash index. For instance, referring to the tables of FIG. 2, a query of SELECT*FROM Table 2 WHERE Client=‘Kalle’ could benefit froma hash index created on the Client column. In this example, the hashindex would be configured such that the column value will be the keyinto the hash index and the actual value mapped to that key would justbe a pointer to the row data in Table 2. Since a hash index is anassociative array, a typical entry can comprise “Kalle=>0x29838”, where0x29838 is a reference to the table row where Kalle is stored in memory.Thus, looking up a value of “Kalle” in a hash index can return areference to the row in memory which is faster than scanning Table 2 tofind all rows with a value of “Kalle” in the Client column. The pointerto the row data enables retrieval of other values in the row.

As shown in FIG. 5B, the logical inference engine 106 can be configuredfor generating one or more bidirectional table indexes (BTI) 502 a, 502b, 502 c, 502 d, and/or 502 e and one or more bidirectional associativeindexes (BAI) 503 a, 503 b, 503 c and/or 503 d based on a data model501. The logical inference engine 106 can scan each table in the datamodel 501 and create the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e. ABTI can be created for each column of each table in the data. The BTI502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise a hash index. TheBTI 502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise firstattributes and pointers to the table rows comprising the firstattributes. For example, referring to the tables of FIG. 2 , an exampleBTI 502 a can comprise “Kalle=>0x29838”, where Kalle is an attributefound in Table 2 and 0x29838 is a reference to the row in Table 2 whereKalle is stored in memory. Thus, the BTI 502 a, 502 b, 502 c, 502 d,and/or 502 e can be used to determine other attributes in other columns(e.g., second attributes, third attributes, etc.) in table rowscomprising the first attributes. Accordingly, the BTI can be used todetermine that an association exists between the first attributes andthe other attributes.

The logical inference engine 106 can scan one or more of BTI 502 a, 502b, 502 c, 502 d, and/or 502 e and create the BAI 503 a, 503 b, 503 cand/or 503 d. The BAI 503 a, 503 b, 503 c, and/or 503 d can comprise ahash index. The BAI 503 a, 503 b, 503 c, and/or 503 d can comprise anindex configured for connecting attributes in a first table to commoncolumns in a second table. The BAI 503 a, 503 b, 503 c, and/or 503 dthus allows for identification of rows in the second table which thenpermits identification of other attributes in other tables. For example,referring to the tables of FIG. 2 , an example BAI 503 a can comprise“Kalle=>0x39838”, where Kalle is an attribute found in Table 2 and0x39838 is a reference to a row in Table 4 that contains Kalle. In anaspect, the reference can be to a hash that can be in-memory or on disk.

Using the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e and the BAI 503a, 503 b, 503 c, and/or 503 d, the logical inference engine 106 cangenerate an index window 504 by taking a portion of the data model 501and mapping it into memory. The portion of the data model 501 taken intomemory can be sequential (e.g., not random). The result is a significantreduction in the size of data required to be loaded into memory.

In an aspect, bidirectional indexing using BTIs can have limits as tohow much parallelization can be applied when processing the data model501. To improve parallelization applied to the data model 501, thelogical inference engine 106 can generate bidirectional indexes forpartitions for a table in the data model 501. Such bidirectional indexesare hereinafter referred to as “indexlets.” In an aspect, the logicalinference engine 106 can generate indexlets for a given table bypartitioning the table into blocks of rows. In an aspect, the blocks ofrows can be of a same size. In an aspect, a last block of rows can be ofa size less than the remaining blocks of rows. In an aspect, afterpartitioning the blocks of rows, the logical inference engine cangenerate an indexlet for each of the blocks of rows. In an aspect,generating an indexlet for a given block of rows comprises generating abidirectional index as described above, but limited in scope to thegiven block of rows.

Provided the input data sources, the logical inference engine 106 canimplement an indexation process (e.g., symbol indexation) to generatesthe indexlets. Indexlets thus generated can serve as a foundation forproviding bi-directional indexing information for the both inferencingand/or hypercube domain calculation techniques.

Given an input data source 102 in an interpretable format, e.g., CSV,the indexation process can begin with partitioning the data source 102into disjoint, same-sized blocks of rows. In some aspects, theindexation process will not partition the last row (e.g., the size ofthe last block might be smaller than the size of the other blocks).These “slices” of the data can be then processed independently togenerate intermediate indexlet structures. Intermediate indexletstructures can be processed sequentially to generate a global symbolmap. In addition to bi-directional information (symbol to row and row tosymbol), a mapping between the symbols can reside locally in theindexlet and in the global symbol map. This mapping enables a simple yetfast and efficient transformation between symbols in an indexlet and inglobal symbol maps and vice versa through select and rank operations onbit vectors.

There are two main challenges to the indexation process: parallelizationof the creation of intermediate indexlet structures and the creation andhandling of large global symbol maps that contain potentially billionsof symbols.

The indexation process can be divided into two components: an indexerservice and a global symbol service. While the indexer service handlesan indexation request as well as distributing tasks of creating theintermediate indexlet structures, the global symbol service enablessplitting global symbol maps across machines. Even in good hash mapimplementations, there is always overhead in memory consumption due tothe management of the internal data structure. As result, the ability tosplit global symbol maps across machines helps to share the load as wellas supporting both horizontal and vertical scaling when dealing withlarge data set.

To achieve the maximum parallelization of the creation of intermediateindexlet structures, the indexer service can utilize a distributedcomputing environment. A master node can comprise information regardingthe capability of worker nodes registered during their initialization.On receiving an indexation request, the master node distributes tasks toworker nodes based on the registered capability. In this setup, moreworker nodes can be dynamically added and registered with the masternode to reduce the required creation time of the intermediate indexletstructures. Moreover, if a worker node dies during the process, a newworker node can be instantiated and registered to the master node totake over the corresponding tasks. The master node can also communicatewith a global symbol master node to get global symbol maps initializedand ready for the global symbol service.

When dealing with large data sets, global symbol maps can comprisebillions of symbols. Naturally, an in-memory hash map can provide betterperformance on both look up and insert operations in comparison tofile-based hash map implementations. Unfortunately, it is not practicalto have an unlimited amount of physical memory available. Althoughvirtual memory can help to elevate the limitation of physical memory,the performance of lookup and insert operations degrades dramatically.

A global symbol service is provided in which global symbol maps aresplit across machines to share the load as well as the stress on memoryrequirements while achieving the desired performance.

FIG. 4 illustrates a global symbol service 410. During an initializationprocess, worker nodes 414 a, 414 b, and 414 c register theircapabilities, e.g., amount of memory, processing power, bandwidth, etc.,with a global master node 412. On receiving an indexation request, forexample from the indexer master node 402, the global symbol master node412 can request initialization of global symbol maps 416 a, 416 b, and416 c on worker nodes 414 a, 414 b, and 414 c based on the registeredcapabilities. As a result, the global symbol maps 416 a, 416 b, and 416c are initialized, and proper capability is reserved accordingly.

The indexer service and the global symbol service can generateintermediate indexlet structures and process the intermediate indexletstructures sequentially to generate the global symbol maps together withbi-directional indexing information. This constraint on processing orderpermits fast and efficient mappings between symbols that reside locallyin an indexlet and the global symbol maps. The global symbol serviceallows parallelism to improve indexation performance.

For example, a state, S, can be introduced into the global symbol maps416 a, 416 b, and 416 c on the worker nodes 414 a, 414 b, and 414 c asfollows S={standing_by, serving, closed}

where “standing_by” indicates that the global symbol map on the workernode is not in use, “serving” indicates that the global symbol map onthe worker node can be used for both lookup and insert operations,“closed” indicates that the global symbol map on the worker node isfull, and, thus, only supports a lookup operation.

The creation of the global symbol map can start with inserting symbolsinto a serving hash map on the corresponding worker node. When theoptimal capacity of the hash map is reached, the corresponding workernode informs the global symbol master node and changes its state toclosed. The global symbol master node can then request another workernode to handle the upcoming tasks, e.g., changing the state of a hashmap from “standing_by” to “serving.” On subsequent processes, lookupoperations can be carried out in a bulk and in a parallelized manner ona closed hash map to maximize the performance. The remaining new symbolscan then be inserted into the serving hash map on the correspondingworker node. If a worker node in “standing_by” state dies during theprocess, it can be replaced by instantiating another worker node thatregisters itself to the master node. If a worker node in “closed” or“serving” state dies, it can be replaced by either another worker nodein “standing_by” state or a newly instantiated worker node. In thiscase, the master node informs the indexer service and the range of thecorresponding data will be indexed again to reconstruct thecorresponding hash map.

In an aspect, a Bloom filter 418 a, 418 b, and 418 c can be used tofurther optimize lookup performance. A Bloom filter is a probabilisticdata structure that can indicate whether an element either definitely isnot in the set or may be in the set. In other words, false-positivematches are possible, but false negatives are not. The base datastructure of a Bloom filter is a bit vector. On a very large hash mapthat contains several billion symbols, the performance of the lookupoperation can degrade dramatically as the size increases. The Bloomfilter is a compact data structure that can represent a set with anarbitrarily large number of elements. The Bloom filter enables fastquerying of the existence of an element in a set. Depending on theregistered resource information, the false positive rate can bespecified to achieve both the compactness of the Bloom filter and theminimum access to the hash map. A Bloom filter can improve theperformance of lookup operation on closed hash map by 3 to 5 times. Theconstructed Bloom filter 418 a, 418 b, and 418 c can be used to minimizethe amount of communication required in the inferencing as well ashypercube domain construction process. Particularly, by performinglookup operations in the Bloom filters 418 a, 418 b, and 418 c first,the number of hash maps that possibly contain the desired informationwill be minimized, and, thus, reduce the number of requests that need tobe transferred through the network.

The indexer service and the global symbol service allows both local aswell as cloud-based deployment of symbol indexation. With thecloud-based deployment, more resources can be added to improve theindexation process. The indexation process is bounded by the amount ofresources and the available bandwidth. In large-scale deployment, directcommunication between indexer worker nodes 404 a, 404 b, and 404 c andglobal symbol worker nodes 414 a, 414 b, and 414 c can be setup toreduce the load on the global symbol master node 412.

A representation of a data structure for indexlets is shown in FIG. 5D.Rows of a given table 550 can be divided into block bidirectionallyindexed by indexlets 552, 554, and 556, respectively. In the example ofFIG. 5D, the indexlet 552 can include pointers or references torespective columns 558, 560, and 562 as set forth above with respect tobidirectional table indexes. Each of the indexlets 552, 554, and 556 arelogically associated with a bidirectional global attribute lists 564 and566 that index a particular attribute to the blocks it is present in.Accordingly, an entry in the bidirectional global attribute list 564 and566 for a given attribute can comprise a reference to an indexletcorresponding to a block having the respective attribute. In an aspect,the reference can include a hash reference. In an aspect, as shown inFIG. 5F, an implicit relationship exists between indexlets in differenttables through a common field present in both tables and anattribute-to-attribute (A2A) index.

Thus, the logical inference engine 106 can determine a data subset basedon user selections. The logical inference engine 106 automaticallymaintains associations among every piece of data in the entire data setused in an application. The logical inference engine 106 can store thebinary state of every field and of every data table dependent on userselection (e.g., included or excluded). This can be referred to as astate space and can be updated by the logical inference engine 106 everytime a selection is made. There is one bit in the state space for everyvalue in the symbol table or row in the data table, as such the statespace is smaller than the data itself and faster to query. The inferenceengine will work associating values or binary symbols into the dimensiontuples. Dimension tuples are normally needed by a hypercube to produce aresult.

The associations thus created by the logical inference engine 106 meansthat when a user makes a selection, the logical inference engine 106 canresolve (quickly) which values are still valid (e.g., possible values)and which values are excluded. The user can continue to make selections,clear selections, and make new selections, and the logical inferenceengine 106 will continue to present the correct results from the logicalinference of those selections. In contrast to a traditional join modeldatabase, the associative model provides an interactive associativeexperience to the user.

FIG. 5C illustrates an example application of BTI's and BAI's todetermine inferred states both inter-table and intra-table using Table 1and Table 2 of FIG. 2 . A BTI 520 can be generated for the “Client”attribute of Table 2. In an aspect, the BTI 520 can comprise an invertedindex 521. In other aspect, the inverted index 521 can be considered aseparate structure. The BTI 520 can comprise a row for each uniqueattribute in the “Client” column of Table 2. Each unique attribute canbe assigned a corresponding position 522 in the BTI 520. In an aspect,the BTI 520 can comprise a hash for each unique attribute. The BTI 520can comprise a column 523 for each row of Table 2. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 520 reflects that the attribute “Nisse” is found in rows 1 and 6of Table 2, the attribute “Gullan” is found in row 2 of Table 2, theattribute “Kalle” is found in rows 3 and 4 of Table 2, and the attribute“Pekka” is found in row 5 of Table 2.

The inverted index 521 can be generated such that each position in theinverted index 521 corresponds to a row of Table 2 (e.g., first positioncorresponds to row 1, second position corresponds to row 2, etc. . . .). A value can be entered into each position that reflects thecorresponding position 522 for each attribute. Thus, in the invertedindex 521, position 1 comprises the value “1” which is the correspondingposition 522 value for the attribute “Nisse”, position 2 comprises thevalue “2” which is the corresponding position 522 value for theattribute “Gullan”, position 3 comprises the value “3” which is thecorresponding position 522 value for the attribute “Kalle”, position 4comprises the value “3” which is the corresponding position 522 valuefor the attribute “Kalle”, position 5 comprises the value “4” which isthe corresponding position 522 value for the attribute “Pekka”, andposition 6 comprises the value “1” which is the corresponding position522 value for the attribute “Nisse”.

A BTI 524 can be generated for the “Product” attribute of Table 2. In anaspect, the BTI 524 can comprise an inverted index 525. In other aspect,the inverted index 525 can be considered a separate structure. The BTI524 can comprise a row for each unique attribute in the “Product” columnof Table 2. Each unique attribute can be assigned a correspondingposition 526 in the BTI 524. In an aspect, the BTI 524 can comprise ahash for each unique attribute. The BTI 524 can comprise a column 527for each row of Table 2. For each attribute, a “1” can indicate thepresence of the attribute in the row and a “0” can indicate an absenceof the attribute from the row. “0” and “1” are merely examples of valuesused to indicate presence or absence. Thus, the BTI 524 reflects thatthe attribute “Toothpaste” is found in row 1 of Table 2, the attribute“Soap” is found in rows 2, 3, and 5 of Table 2, and the attribute“Shampoo” is found in rows 4 and 6 of Table 2.

By way of example, the inverted index 525 can be generated such thateach position in the inverted index 525 corresponds to a row of Table 2(e.g., first position corresponds to row 1, second position correspondsto row 2, etc.). A value can be entered into each position that reflectsthe corresponding position 526 for each attribute. Thus, in the invertedindex 525, position 1 comprises the value “1” which is the correspondingposition 526 value for the attribute “Toothpaste”, position 2 comprisesthe value “2” which is the corresponding position 526 value for theattribute “Soap”, position 3 comprises the value “2” which is thecorresponding position 526 value for the attribute “Soap”, position 4comprises the value “3” which is the corresponding position 526 valuefor the attribute “Shampoo”, position 5 comprises the value “2” which isthe corresponding position 526 value for the attribute “Soap”, andposition 6 comprises the value “3” which is the corresponding position526 value for the attribute “Shampoo.”

By way of example, a BTI 528 can be generated for the “Product”attribute of Table 1. In an aspect, the BTI 528 can comprise an invertedindex 529. In other aspect, the inverted index 529 can be considered aseparate structure. The BTI 528 can comprise a row for each uniqueattribute in the “Product” column of Table 1. Each unique attribute canbe assigned a corresponding position 530 in the BTI 528. In an aspect,the BTI 528 can comprise a hash for each unique attribute. The BTI 528can comprise a column 531 for each row of Table 1. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 528 reflects that the attribute “Soap” is found in row 1 ofTable 1, the attribute “Soft Soap” is found in row 2 of Table 1, and theattribute “Toothpaste” is found in rows 3 and 4 of Table 1.

By way of example, the inverted index 529 can be generated such thateach position in the inverted index 529 corresponds to a row of Table 1(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 530 for each attribute. Thus, in theinverted index 529, position 1 comprises the value “1” which is thecorresponding position 530 value for the attribute “Soap”, position 2comprises the value “2” which is the corresponding position 530 valuefor the attribute “Soft Soap”, position 3 comprises the value “3” whichis the corresponding position 530 value for the attribute “Toothpaste”,and position 4 comprises the value “3” which is the correspondingposition 530 value for the attribute “Toothpaste”.

By way of example, a BAI 532 can be generated as an index between theproduct attribute of Table 2 and Table 1. The BAI 532 can comprise a rowfor each unique attribute in the BTI 524 by order of correspondingposition 526. The value in each row can comprise the correspondingposition 530 of the BTI 528. Thus, position 1 of the BAI 532 correspondsto “Toothpaste” in the BTI 524 (corresponding position 526 of 1) andcomprises the value “3” which is the corresponding position 530 for“Toothpaste” of the BTI 528. Position 2 of the BAI 532 corresponds to“Soap” in the BTI 524 (corresponding position 526 of 2) and comprisesthe value “1” which is the corresponding position 530 for “Soap” of theBTI 528. Position 3 of the BAI 532 corresponds to “Shampoo” in the BTI524 (corresponding position 526 of 3) and comprises the value “−1” whichindicates that the attribute “Shampoo” is not found in Table 1.

By way of example, a BAI 533 can be created to create an index betweenthe product attribute of Table 1 and Table 2. The BAI 533 can comprise arow for each unique attribute in the BTI 528 by order of correspondingposition 530. The value in each row can comprise the correspondingposition 526 of the BTI 524. Thus, position 1 of the BAI 533 correspondsto “Soap” in the BTI 528 (corresponding position 530 of 1) and comprisesthe value “2” which is the corresponding position 526 for “Soap” of theBTI 524. Position 2 of the BAI 533 corresponds to “Soft Soap” in the BTI528 (corresponding position 530 of 2) and comprises the value “−1” whichindicates that the attribute “Soft Soap” is not found in Table 2.Position 3 of the BAI 533 corresponds to “Toothpaste” in the BTI 528(corresponding position 530 of 3) and comprises the value “1” which isthe corresponding position 526 for “Toothpaste” of the BTI 524.

FIG. 5C illustrates an example application of the logical inferenceengine 106 utilizing the BTI 520, the BTI 524, and the BTI 528. A usercan select the “Client” “Kalle” from within a user interface. A columnfor a user selection 534 of “Kalle” can be indicated in the BTI 520comprising a value for each attribute that reflects the selection statusof the attribute. Thus, the user selection 534 comprises a value of “0”for the attribute “Nisse” indicating that “Nisse” is not selected, theuser selection 534 comprises a value of “0” for the attribute “Gullan”indicating that “Gullan” is not selected, the user selection 534comprises a value of “1” for the attribute “Kalle” indicating that“Kalle” is selected, and the user selection 534 comprises a value of “0”for the attribute “Pekka” indicating that “Pekka” is not selected.

The BTI 520 can be consulted to determine that the attribute “Kalle” hasa value of “1” in the column 523 corresponding to rows 3 and 4. In anaspect, the inverted index 521 can be consulted to determine that theuser selection 534 relates to the position 522 value of “3” which isfound in the inverted index 521 at positions 3 and 4, implicating rows 3and 4 of Table 1. Following path 535, a row state 536 can be generatedto reflect the user selection 534 as applied to the rows of Table 2. Therow state 536 can comprise a position that corresponds to each row and avalue in each position reflecting whether a row was selected. Thus,position 1 of the row state 536 comprises the value “0” indicating thatrow 1 does not contain “Kalle”, position 2 of the row state 536comprises the value “0” indicating that row 2 does not contain “Kalle”,position 3 of the row state 536 comprises the value “1” indicating thatrow 3 does contain “Kalle”, position 4 of the row state 536 comprisesthe value “1” indicating that row 4 does contain “Kalle”, position 5 ofthe row state 536 comprises the value “0” indicating that row 5 does notcontain “Kalle”, and position 6 of the row state 536 comprises the value“0” indicating that row 6 does not contain “Kalle”.

Following path 537, the row state 536 can be compared with the invertedindex 525 to determine the corresponding position 526 contained in theinverted index 525 at positions 3 and 4. The inverted index 525comprises the corresponding position 526 value of “2” in position 3 andthe corresponding position 526 value of “3” in position 4. Followingpath 538, the corresponding position 526 values of “2” and “3” can bedetermined to correspond to “Soap” and “Shampoo” respectively in the BTI524. Thus, the logical inference engine 106 can determine that both“Soap” and “Shampoo” in Table 2 are associated with “Kalle” in Table 2.The association can be reflected in an inferred state 539 in the BTI524. The inferred state 539 can comprise a column with a row for eachattribute in the BTI 524. The column can comprise a value indicated theselection state for each attribute. The inferred state 539 comprises a“0” for “Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”, the inferred state 539 comprises a “1” for “Soap” indicatingthat “Soap” is associated with “Kalle”, and inferred state 539 comprisesa “1” for “Shampoo” indicating that “Shampoo” is associated with“Kalle”.

Following path 540, the inferred state 539 can be compared to the BAI532 to determine one or more associations between the selection of“Kalle” in Table 2 and one or more attributes in Table 1. As theinferred state 539 comprises a value of “1” in both position 2 andposition 3, the BAI 532 can be assessed to determine the valuescontained in position 2 and position 3 of the BAI 532 (following path541). Position 2 of the BAI 532 comprises the value “1” which identifiesthe corresponding position 530 of “Soap” and position 3 of the BAI 532comprises the value “−1” which indicates that Table 1 does not contain“Shampoo”. Thus, the logical inference engine 106 can determine that“Soap” in Table 1 is associated with “Kalle” in Table 2. The associationcan be reflected in an inferred state 542 in the BTI 528. The inferredstate 542 can comprise a column with a row for each attribute in the BTI528. The column can comprise a value indicated the selection state foreach attribute. The inferred state 542 comprises a “1” for “Soap”indicating that “Soap” is associated with “Kalle”, the inferred state542 comprises a “0” for “Soft Soap” indicating that “Soft Soap” is notassociated with “Kalle”, and the inferred state 542 comprises a “0” for“Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”. Based on the current state of BTIs and BAIs, if the datasources 102 indicate that an update or delta change has occurred to theunderlying data, the BTIs and BAIs can be updated with correspondingchanges to maintain consistency.

In aspects implementing indexlets, the logical inference engine 106 canapply query language by first performing intra-table inferencing onrespective tables. Intra-table inferencing comprises transferring theimposed state of one field to other fields within the same table. In anaspect, intra-table inferencing can comprise computing the union of theindex of the active attributes in a user input 504. The intersection ofthe result of the union operation and record states (i.e. row states510) is then determined. This result is then intersected with theattribute states 514 of other columns using the inverted index 512. Ifother selection vectors from a previously provided user input vector 504has zero active entries, a conflict can be detected. In an aspect, thelogical inference engine 106 can resolve the detected conflict. In anaspect, resolving a conflict can include deleting or otherwiseeliminating one or more incompatible selections. In another aspect,resolving a conflict can include reverting the data model 501 or aportion of the data model 501, e.g. a table, record, or attribute, to aprevious state.

In an aspect, after performing intra-table inferencing, the logicalinference engine 106 can perform inter-table inferencing based on theintra-table inferencing output of a plurality of tables, as is depictedin FIG. 5E. In an aspect, intra-table inferencing can includetransferring a common field attribute of one table 569 to a child in itsbranch. In an aspect, this can be performed by running the attributestates 570 output from intra-table inferencing through anattribute-to-attribute (A2A) index 572 referencing the attribute states574 in a second table 576. In an aspect, the A2A index 572 can bepartitioned into one or more indexlets as described herein with respectto other data tables. In another aspect, transferring a common fieldattribute of one table 569 to a child in its branch by running theattribute states 570 output from intra-table inferencing through afunction or logic performing similar functionality as the A2A index 572.For example, a function, service, or other logic can accept as input apair of symbols and return an indication of whether or not they arerelated, e.g. TRUE or FALSE. In another aspect, attribute-to-attributerelations can be indicated by user input.

Based on current selections and possible rows in data tables acalculation/chart engine 108 can calculate aggregations in objectsforming transient hypercubes in an application. The calculation/chartengine 108 can further build a virtual temporary table from whichaggregations can be made. The calculation/chart engine 108 can perform acalculation (e.g., evaluate an expression in response to a userselection/de-selection) via a multithreaded operation. The state-spacecan be queried to gather all of the combinations of dimensions andvalues necessary to perform the calculation. In an aspect, the query canbe on one thread per object, one process, one worker, combinationsthereof, and the like. The expression can be calculated on multiplethreads per object. Results of the calculation can be passed to arendering engine 116 and/or optionally to an extension engine 110.

In an aspect, the chart engine 108 can receive dimensions, expressions,and sorting parameters and can compute a hypercube data structurecontaining aggregations along the dimensions. For example, a virtualrecord can be built with a placeholder for all field values (or indices)needed, as a latch memory location. When all values are assigned, thevirtual record can be processed to aggregate the fields needed forcomputations and save the dimension values in a data structure per rowof the resulting hypercube. In such a way, the traversal of the databasecan be done in an arbitrary way, just depending on requirements providedby memory consumption and indexing techniques used for the particularcase at hand.

In an aspect, any aggregation function processed by the associative dataindexing engine 100 can be qualified to operate on a subset of records(rather than a current selection of data records and/or all datarecords). The associative data indexing engine 100 can definealternative aggregation sets based on set analysis (e.g., setexpression, etc.). Using set analysis, the associative data indexingengine 100 can support methods to define an aggregation set. The exactcompositions of defined aggregation sets may not only depend on desiredconditions but also the chart (analysis) they are used in. Theassociative data indexing engine 100 may execute/perform set analysis(e.g., set expression analysis, etc.) for one or more set expressionsdetermined/extracted from a query, such as an undetermined query (e.g.,an imprecise query, an undefined query, an incomplete query, a partiallyexpressed query, a portioned query, etc.), to determine and/or define anaggregation set.

To define an aggregation set for an undetermined query (e.g., animprecise query, an undefined query, an incomplete query, a partiallyexpressed query, a portioned query, etc.), the associative data indexingengine 100 may consider and/or account for items (e.g., compositionalelements, predicates, etc.), constraints (e.g., data constraints,logical constraints, etc.) of the query, and one or more data analysismodels (e.g., data charts, data tables, data graphs, data maps,graphical objects, key performance indicators (KPIs), etc.).

For example, the associative data indexing engine 100 may determine howeach input item and/or computational element fits a data analysis modelbased on the data analysis model's capacity and/or projectability of anitem (e.g., whether it has any condition, whether the condition resultson one or multiple values, etc.). For example, the associative dataindexing engine 100 may determine an optimal data analysis model fromone or more data analysis models determined (e.g., via the chart engine108, etc.) from an undetermined query that best fits each input itemand/or computational element.

For example, the associative data indexing engine 100 may define anaggregation set for each of the following undetermined business-relatedqueries:

-   -   Query 1: Sales by product where sales>2000    -   Query 2: Products with sales>2000    -   Query 3: Number of products with sales>2000

Query 1, Query 2, and Query 3, each include similar (e.g., conceptuallysimilar, etc.) items (e.g., compositional elements, predicates, etc.),such as “sales,” “products,” “>2000,” and/or the like. The associativedata indexing engine 100 may, for example, use natural language parsingand/or metadata analysis to determine the items and/or any constraintsof the query, such as a default analysis period, a required data/elementselection, and/or the like. The computing device may determine/perform adifferent set analysis for the Query 1, the Query 2, the Query 3, and/orany other undetermined query based on, for example, an order/arrangementof items (and/or computational elements/constraints) of the query and/orthe composition (e.g., dimensions, measures, etc.) of one or more dataanalysis models (e.g., data charts, data tables, data graphs, data maps,graphical objects, key performance indicators (KPIs), etc.). Thecomputing device may determine/perform a different set analysis forQuery 1, Query 2, and Query 3 (and/or any other query) according tonovel algorithms described herein.

Compositional elements (e.g., predicates, conditions, data constraints,etc.) of a query and/or query data may be determined. Compositionalelements (e.g., predicates, conditions, data constraints, etc.) of thequery data may include and/or be based on text/items from the query andcorresponding conditional predicate(s). For example, for Query 1, Query2, and Query 3, the associative data indexing engine 100 may determineexample compositional elements shown below:

Predicate Condition Sales item >2000 ProductName N/A (no condition)

Metadata for and/or associated with an undetermined query and/or anycompositional element for the undetermined query may be determined. Forexample, semantic data types may uniformly represent standard datatypes, compositional elements, validations, formatting rules, and otherbusiness logic that may be further used to determine and/or define anaggregation set. Semantic types may be stored as metadata structuresthat may be used and reused during the process of query analysis.

For example, for Query 1, Query 2, and/or Query 3, the associative dataindexing engine 100 may use undetermined query semantic types and/or thelike to determine/define a default analysis specified for certain facts(e.g., including Sales, etc.) and/or certain facts may be preconditionedto making a selection on certain compositional elements and/orcategories of the query. For example, for Query 1, Query 2, and/or Query3, the associative data indexing engine 100 may determine and/or assumeSales (e.g., number of product sales, etc.) are expected to beconstrained to a current quarter (and/or an extra condition such asQuartersAgo=0).

A set of input items and/or computational elements may be adjusted, forexample by the associative data indexing engine 100, to ensure there isno conflict. A query, such as a natural question, may include anexplicit time frame, for example, an undetermined query may be “Sales byproduct, where sales>2000 in 2019,” where the year 2019 is the explicittime frame for the query. The associative data indexing engine 100 maydetermine that any default time period is unwarranted and/or if theglobal selections already satisfy any metadata-driven preconditions touse a measure.

The best data analysis model for a query may be determined. For example,the best data analysis model may be a data analysis model most relevantto a query—determined based on how aggregated related data maypotentially fit and/or apply to elements, fields, constraints,components, and/or the like of a data analysis model. For example, inputitems and/or computational elements associated with a rank and/orranking may be best fitted to a bar chart and/or related data analysismodel, input items and/or computational elements associated with valuesmay be best fitted to a table, input items and/or computational elementsassociated with facts may be best fitted to a KPI and/or related dataanalysis model.

The associative data indexing engine 100 may determine, for example, adata analysis model most relevant to a query based on the analysis'capacity and also the projectability of an item and/or compositionalelement and of a query, such as whether the item and/or compositionalelement is associated with any condition, and/or whether the conditionresults on one or multiple values. For example, the associative dataindexing engine 100 may determine that a rank analysis may accommodateone measure and one dimension. For example, a rank analysis and/orassociated data analysis model may be determined for Query 1 (Sales byproduct where sales>2000) because a rank analysis and/or associated dataanalysis model may include “sales and measure,” and “product” asdimensions. However, for a slightly modified query such as:

Modified Query 1: Sales by product in Nordic countries where Sales>2000;there are two dimensions, “product” and “country,” to choose from, and“product” has no condition which gives it an edge over “country.” Insuch a situation, the associative data indexing engine 100 may combineitems, compositional elements, and/or constraints and determine/generateset expressions. A final set of compositional elements and/orconstraints may be combined, for example by the query analysis module105, for further analysis, for example, by the chart engine 108.

For example, for an undetermined query, such as Query 1, Query 2, and/orQuery 3, all of the combinations of dimensions and values necessary toperform the calculation may include any conditional element and/orconstraint of the query. The conditional element and/or constraint ofthe query may be applied to each measure by injecting a set modifierinto a corresponding aggregation function. For example, the measure fora rank analysis may be as follows:

=sum({<Set1, Set2>} Sales),

where:

Set1=[Product]={′=Sum({<[QuartersAgo]={0}>} Sales>2000)′ }; andSet12=[QuartersAgo]={0}.

Notably, the aggregation of Sales is further modified by a set ofproducts whose sales are greater than 2000 (hence the second innerself-aggregation). For an analysis with no Measure (e.g., onlydimensions, etc.), essentially the same pattern may be used. However,constraints (and/or conditional elements) are applied to instances ofthe projecting dimension(s). For example, for Query 2 (Products withsales>2000), the expression for Product may be as follows:

=Aggr(if (sum({<QuartersAgo]={0}>} Sales)>2000 Only(Product)), Product).

Analysis with no dimension may require, for most cases, the measureexpression to be adjusted by using other constraints (and/or conditionalelements) as simple attribute constraints. For example, for anundetermined query such as “Sales where costs>1000,” the condition onCost may be applied at leaf level (no aggregation). A set expression maybe as follows:

=Sum({<Cost={“>1000”}>} Sales)

However, an exception may occur. The exception is when an explicitdimension, such as “Product” from Query 3 (Number of products withsales>2000), may be found. This happens only for aggregation ofdistinct-count. If so, the measure constraints may be properlyaggregated and applied, for example, as follows:

=count(distinct{<Product={′=Sum({<({<[QuartersAgo]={0}>} Sales)>2000′},({<[QuartersAgo]={0}>} Product).

The methods provided can be implemented by means of a computer programas illustrated in a flowchart of a method 300 in FIG. 3 . In a step 302,the program can read some or all data records in the database, forinstance using a SELECT statement which selects all the tables of thedatabase, e.g. Tables 1-5. In an aspect, the database can be read intoprimary memory of a computer.

To increase evaluation speed, each unique value of each data variable insaid database can be assigned a different binary code and the datarecords can be stored in binary-coded form. This can be performed, forexample, when the program first reads the data records from thedatabase. For each input table, the following steps can be carried out.The column names, e.g. the variables, of the table can be read (e.g.,successively). Every time a new data variable appears, a data structurecan be instantiated for the new data variable. An internal tablestructure can be instantiated to contain some or all the data records inbinary form, whereupon the data records can be read (e.g., successively)and binary-coded. For each data value, the data structure of thecorresponding data variable can be checked to establish if the value haspreviously been assigned a binary code. If so, that binary code can beinserted in the proper place in the above-mentioned table structure. Ifnot, the data value can be added to the data structure and assigned anew binary code, for example the next binary code in ascending order,before being inserted in the table structure. In other words, for eachdata variable, a unique binary code can be assigned to each unique datavalue.

After having read some or all data records in the database, the method300 can analyze the database in a step 304 to identify all connectionsbetween the data tables. A connection between two data tables means thatthese data tables have one variable in common. In an aspect, step 304can comprise generation of one or more bidirectional table indexes andone or more bidirectional associative indexes. In an aspect, generationof one or more bidirectional table indexes and one or more bidirectionalassociative indexes can comprise a separate step. In another aspect,generation of one or more bidirectional table indexes and one or morebidirectional associative indexes can be on-demand. After the analysis,all data tables are virtually connected. In FIG. 2 , such virtualconnections are illustrated by double-ended arrows. The virtuallyconnected data tables can form at least one so-called “snowflakestructure,” a branching data structure in which there is one and onlyone connecting path between any two data tables in the database. Thus, asnowflake structure does not contain any loops. If loops do occur amongthe virtually connected data tables, e.g. if two tables have more thanone variable in common, a snowflake structure can in some cases still beformed by means of special algorithms known in the art for resolvingsuch loops.

After this initial analysis, the user can explore the database and/ordefine a mathematical function. Assume that the user wants to extractthe total sales per year and client from the database in FIG. 2 . Theuser defines a corresponding mathematical function “SUM (x*y)”, andselects the calculation variables to be included in this function:“Price” and “Number.” The user also selects the classificationvariables: “Client” and “Year.”

Optionally, a mathematical function may be determined for anundetermined query (e.g., an imprecise query, an undefined query, anincomplete query, a partially expressed query, a portioned query, etc.),for example, “Sales by product where sales>2000.” Calculation variablesfor the undetermined query may include “Product,” “Price,” “Date,” and“Year.”

At step 306, a mathematical function may be determined. The mathematicalfunction may be, for example, a combination of mathematical expressions.For example, an undetermined query such as “Sales by product wheresales>2000,” can be used to extract the total sales of a product wherethe number (e.g., sale amount, etc.) exceeds 2000. A correspondingmathematical function may be defined, for example by a user as:

=sum({<Set1, Set2>} Sales),

where:

Set1=[Product]={′=Sum({<[QuartersAgo]={0}>} Sales>2000)′}; andSet12=[QuartersAgo]={0}.

Calculation variables to be included in this function may include“Product” and “Number.” The classification variable “Year” may also beset, for example, by a user.

The method 300 then identifies in step 308 all relevant data tables,e.g. all data tables containing any one of the selected calculation andclassification variables, such data tables being denoted boundarytables, as well as intermediate data tables in the connecting path(s)between these boundary tables in the snowflake structure, such datatables being denoted connecting tables. There are no connecting tablesin the present example. In an aspect, one or more bidirectional tableindexes and one or more bidirectional associative indexes can beaccessed as part of step 308.

In the present example, all occurrences of every value, e.g. frequencydata, of the selected calculation variables can be included forevaluation of the mathematical function. In FIG. 2 , the selectedvariables (“Price,” “Number”) can require such frequency data. Now, asubset (B) can be defined that includes all boundary tables (Tables 1-2)containing such calculation variables and any connecting tables betweensuch boundary tables in the snowflake structure. It should be noted thatthe frequency requirement of a particular variable is determined by themathematical expression in which it is included. Determination of anaverage or a median calls for frequency information. In general, thesame is true for determination of a sum, whereas determination of amaximum or a minimum does not require frequency data of the calculationvariables. It can also be noted that classification variables in generaldo not require frequency data.

Then, a starting table can be selected in step 310, for example, amongthe data tables within subset (B). In an aspect, the starting table canbe the data table with the largest number of data records in thissubset. In FIG. 2 , Table 2 can be selected as the starting table. Thus,the starting table contains selected variables (“Client,” “Number”), andconnecting variables (“Date,” “Product”). These connecting variableslink the starting table (Table 2) to the boundary tables (Tables 1 and3).

Thereafter, a conversion structure can be built in step 312. Thisconversion structure can be used for translating each value of eachconnecting variable (“Date,” “Product”) in the starting table (Table 2)into a value of a corresponding selected variable (“Year,” “Price”) inthe boundary tables (Table 3 and 1, respectively). A table of theconversion structure can be built by successively reading data recordsof Table 3 and creating a link between each unique value of theconnecting variable (“Date”) and a corresponding value of the selectedvariable (“Year”). It can be noted that there is no link from value 4(“Date: 1999 Jan. 12”), since this value is not included in the boundarytable. Similarly, a further table of the conversion structure can bebuilt by successively reading data records of Table 1 and creating alink between each unique value of the connecting variable (“Product”)and a corresponding value of the selected variable (“Price”). In thisexample, value 2 (“Product: Toothpaste”) is linked to two values of theselected variable (“Price: 6.5”), since this connection occurs twice inthe boundary table. Thus, frequency data can be included in theconversion structure. Also note that there is no link from value 3(“Product: Shampoo”).

When the conversion structure has been built, a virtual data record canbe created. Such a virtual data record accommodates all selectedvariables (“Client,” “Year,” “Price,” “Number”) in the database. Inbuilding the virtual data record, a data record is read in step 314 fromthe starting table (Table 2). Then, the value of each selected variable(“Client”, “Number”) in the current data record of the starting tablecan be incorporated in the virtual data record in a step 316. Also, byusing the conversion structure each value of each connecting variable(“Date”, “Product”) in the current data record of the starting table canbe converted into a value of a corresponding selected variable (“Year”,“Price”), this value also being incorporated in the virtual data record.

In step 318 the virtual data record can be used to build an intermediatedata structure. Each data record of the intermediate data structure canaccommodate each selected classification variable (dimension) and anaggregation field for each mathematical expression implied by themathematical function. The intermediate data structure can be builtbased on the values of the selected variables in the virtual datarecord. Thus, each mathematical expression can be evaluated based on oneor more values of one or more relevant calculation variables in thevirtual data record, and the result can be aggregated in the appropriateaggregation field based on the combination of current values of theclassification variables (“Client,” “Year”).

The above procedure can be repeated for one or more additional (e.g.,all) data records of the starting table. In a step 320 it can be checkedwhether the end of the starting table has been reached. If not, theprocess can be repeated from step 314 and further data records can beread from the starting table. Thus, an intermediate data structure canbe built by successively reading data records of the starting table, byincorporating the current values of the selected variables in a virtualdata record, and by evaluating each mathematical expression based on thecontent of the virtual data record. If the current combination of valuesof classification variables in the virtual data record is new, a newdata record can be created in the intermediate data structure to holdthe result of the evaluation. Otherwise, the appropriate data record israpidly found, and the result of the evaluation is aggregated in theaggregation field.

Thus, data records can be added to the intermediate data structure asthe starting table is traversed. The intermediate data structure can bea data table associated with an efficient index system, such as an AVLor a hash structure. The aggregation field can be implemented as asummation register, in which the result of the evaluated mathematicalexpression is accumulated.

In some aspects, e.g. when evaluating a median, the aggregation fieldcan be implemented to hold all individual results for a uniquecombination of values of the specified classification variables. Itshould be noted that only one virtual data record is needed in theprocedure of building the intermediate data structure from the startingtable. Thus, the content of the virtual data record can be updated foreach data record of the starting table. This can minimize the memoryrequirement in executing the computer program.

After traversing the starting table, the intermediate data structure cancontain a plurality of data records. If the intermediate data structureaccommodates more than two classification variables, the intermediatedata structure can, for each eliminated classification variable, containthe evaluated results aggregated over all values of this classificationvariable for each unique combination of values of remainingclassification variables.

In an aspect, step 322 can involve any of the processes describedpreviously with regard to FIG. 5A-5F as part of a process for creatingthe hypercube/multidimensional cube. For example, output from thelogical inference engine 18 and/or 106 utilizing one or more BTIs and orone or more A2A indexes can be used in creation of thehypercube/multidimensional cube. When a user makes a selection, theinference engine 18 and/or 106 calculates a data subset of which one ormore BTIs and/or A2A indexes can be generated and provided to the chartengine 58 and/or 108 for use in generating a hypercube/multidimensionalcube and/or evaluating one or more expressions against ahypercube/multidimensional cube via one or more BTIs and/or A2A indexesas described with regard to FIG. 5A-5F.

In an aspect, when the intermediate data structure has been built, afinal data structure(s), e.g., data analysis model(s) (e.g., datacharts, data tables, data graphs, data maps, key performance indicators(KPIs), etc.), may be created by evaluating the mathematical functionbased on the results of the mathematical expression contained in theintermediate data structure. In doing so, the results in the aggregationfields for each unique combination of values of the classificationvariables may be combined.

The data analysis model may be a best fit data analysis model, forexample, a data analysis model that best fits compositional elements(e.g., predicates, conditions, data constraints, etc.) of anundetermined query and/or any other query. As explained, a data analysismodel most relevant to a query may be based on the analysis' capacityand also the projectability of an item and/or compositional element andof the query, such as whether the item and/or compositional element isassociated with any condition, and/or whether the condition results onone or multiple values.

In the example, the creation of the final data structure isstraightforward, due to the trivial nature of the present mathematicalfunction. At step 324, the content of the final data structure may bepresented to the user, for example, in a corresponding data analysismodel, as shown in FIGS. 5G-5I.

FIG. 5G is a best fit data analysis model corresponding to Query 1(Sales by product where sales>2000). FIG. 5G shows that a bar chart isan optimal data analysis model for Query 1 based on the compositionalelements of the query. FIG. 5H is a best fit data analysis modelcorresponding to Query 2 (Products with sales>2000). FIG. 5H shows thata data table is an optimal data analysis model for Query 2 based on thecompositional elements of the query. FIG. 5I is a best fit data analysismodel corresponding to Query 3 (Number of products with sales>2000).FIG. 5I shows that a key performance indicator (KPI) is an optimal dataanalysis model for Query 3 based on the compositional elements of thequery.

At step 326, input from the user can be received. For example, inputfrom the user can be a selection and/or de-selection of the presentedresults.

Optionally, input from the user at step 326 can comprise a request forexternal processing. In an aspect, the user can be presented with anoption to select one or more external engines to use for the externalprocessing. Optionally, at step 328, data underlying the user selectioncan be configured (e.g., formatted) for use by an external engine.Optionally, at step 330, the data can be transmitted to the externalengine for processing and the processed data can be received. Thereceived data can undergo one or more checks to confirm that thereceived data is in a form that can be appended to the data model. Forexample, one or more of an integrity check, a format check, acardinality check, combinations thereof, and the like. Optionally, atstep 332, processed data can be received from the external engine andcan be appended to the data model as described herein. In an aspect, thereceived data can have a lifespan that controls how long the receiveddata persists with the data model. For example, the received data can beincorporated into the data model in a manner that enables a user toretrieve the received data at another time/session. In another example,the received data can persist only for the current session, making thereceived data unavailable in a future session.

FIG. 5A shows how an undetermined query 50 (e.g., an imprecise query, anundefined query, an incomplete query, a partially expressed query, aportioned query, etc.) operates and/or is executed on a data/information52 to generate a data subset 54. The data subset 54 can form a statespace, which is based on the undetermined query 50. In an aspect, thestate space (or “user state”) may be defined by a user providing queryinformation via a user interface of an application. For example, thestate space may be based on any of the following undeterminedbusiness-related queries:

-   -   Query 1: Sales by product where sales>2000    -   Query 2: Products with sales>2000    -   Query 3: Number of products with sales>2000

Query 1, Query 2, and Query 3, each include similar (e.g., conceptuallysimilar, etc.) items (e.g., compositional elements, predicates, etc.),such as “sales,” “products,” “>2000,” and/or the like. Natural languageparsing and/or metadata analysis may be used to determine the itemsand/or any constraints of the query, such as a default analysis period,a required data/element selection, and/or the like. A different setanalysis may be performed for Query 1, Query 2, Query 3, and/or anyother undetermined query based on, for example, an order/arrangement ofitems (and/or constraints) of the query and/or the composition (e.g.,dimensions, measures, etc.) of one or more data analysis models (e.g.,data charts, data tables, data graphs, data maps, graphical objects, keyperformance indicators (KPIs), etc.).

One or more items and/or data constraints of an undetermined query maybe used to determine one or more data analysis models. An applicationcan be designed to host a number of data analysis models (e.g., datacharts, data tables, data graphs, data maps, graphical objects, keyperformance indicators (KPIs), etc.) that evaluate one or moremathematical functions (also referred to as an “expression”) on the datasubset 54 for one or more dimensions (classification variables). Theresult of this evaluation creates a data analysis model result 56.

As illustrated in FIG. 5A, when a user selection, such as anundetermined query 50, is received, the inference engine 18 calculates adata subset. Also, an identifier ID1 for the selection together with thescope can be generated based on the filters in the selection and thescope. Subsequently, an identifier ID2 for the data subset is generatedbased on the data subset definition, for example, a bit sequence thatdefines the content of the data subset. ID2 can be put into a cacheusing ID1 as a lookup identifier. Likewise, the data subset definitioncan be put in the cache using ID2 as a lookup identifier.

As shown in FIG. 5A, a chart (and/or any other data analysis model)calculation in a calculation/chart engine 58 takes place in a similarway. Here, there are two information sets: the data subset 54 andrelevant chart (and/or any other data analysis model) properties 60. Thelatter can be, but not restricted to, a mathematical function togetherwith calculation variables and classification variables (dimensions).For example, for an undetermined query (e.g., Query 1, Query 2, Query 3,etc.) combinations of dimensions and values necessary to perform acalculation may include any conditional element and/or constraint of thequery. The conditional element and/or constraint of the query may beapplied to each measure by injecting a set modifier into a correspondingaggregation function. For example, the measure for a rank analysis maybe as follows:

=sum({<Set1, Set2>} Sales),

where:

Set1=[Product]={′=Sum({<[QuartersAgo]={0}>} Sales>2000)′ }; andSet12=[QuartersAgo]={0}.

Notably, the aggregation of Sales is further modified by a set ofproducts whose sales are greater than 2000 (hence the second innerself-aggregation). For an analysis with no Measure (e.g., onlydimensions, etc.), essentially the same pattern may be used. However,constraints (and/or conditional elements) are applied to instances ofthe projecting dimension(s). For example, for Query 2 (Products withsales>2000), the expression for Product may be as follows:

=Aggr(if (sum({<QuartersAgo]={0}>} Sales)>2000 Only(Product)), Product).

Analysis with no dimension may require, for most cases, the measureexpression to be adjusted by using other constraints (and/or conditionalelements) as simple attribute constraints. For example, for anundetermined query such as “Sales where costs>1000,” the condition onCost may be applied at leaf level (no aggregation). A set expression maybe as follows:

=Sum({<Cost={“>1000”}>} Sales)

However, an exception may occur. The exception is when an explicitdimension, such as “Product” from Query 3 (Number of products withsales>2000), may be found. This happens only for aggregation ofdistinct-count. If so, the measure constraints may be properlyaggregated and applied, for example, as follows:

=count(distinct{<Product={′=Sum({<({<[QuartersAgo]={0}>} Sales)>2000′},({<[QuartersAgo]={0}>} Product).

Mathematical functions together with calculation variables andclassification variables (dimensions) can be used to calculate the chartresult 56, and both of these information sets can be also used togenerate identifier ID3 for the input to the chart calculation. ID2 canbe generated already in the previous step, and ID3 can be generated asthe first step in the chart calculation procedure.

The identifier ID3 can be formed from ID2 and the relevant chartproperties. ID3 can be seen as an identifier for a specific chartgeneration instance, which can include all information needed tocalculate a specific chart result. In addition, a chart resultidentifier ID4 can be created from the chart result definition, forexample, a bit sequence that defines the chart result 56. ID4 can be putin the cache using ID3 as a lookup identifier. Likewise, the chartresult definition can be put in the cache using ID4 as a lookupidentifier.

Optionally, further calculations, transforming, and/or processing can beincluded through an extension engine 62. Optionally, associated resultsfrom the inference engine 18 and further computed by hypercubecomputation in said calculation/chart engine 58 can be coupled to anexternal engine 64 that can comprise one or more data processingapplications (e.g., simulation applications, statistical applications,mathematical computation applications, database applications,combinations thereof, and the like). Context of a data model processedby the inference engine 18 can comprise a tuple or tuples of valuesdefined by dimensions and expressions computed by hypercube routines.Data can be exchanged through an interface 66.

The associated results coupled to the external engine 64 can beintermediate. Further results that can be final hypercube results canalso be received from the external engine 64. Further results can be fedback to be included in the Data/Scope 52 and enrich the data model. Thefurther results can also be rendered directly to the user in the chartresult 56. Data received from and computed by the external engine 64 canbe used for further associative discovery.

Each of the data elements of the database shown in Tables 1-5 of FIG. 2has a data element type and a data element value (for example “Client”is the data element type and “Nisse” is the data element value).Multiple records can be stored in different database structures such asdata cubes, data arrays, data strings, flat files, lists, vectors, andthe like; and the number of database structures can be greater than orequal to one and can comprise multiple types and combinations ofdatabase structures. While these and other database structures can beused with, and as part of, the methods and systems disclosed, theremaining description will refer to tables, vectors, strings, and datacubes solely for convenience.

Additional database structures can be included within the databaseillustrated as an example herein, with such structures includingadditional information pertinent to the database such as, in the case ofproducts for example; color, optional packages, etc. Each table cancomprise a header row which can identify the various data element types,often referred to as the dimensions or the fields, that are includedwithin the table. Each table can also have one or more additional rowswhich comprise the various records making up the table. Each of the rowscan contain data element values (including null) for the various dataelement types comprising the record.

The database as referred to in Tables 1-5 of FIG. 2 can be queried byspecifying the data element types and data element values of interestand by further specifying any functions to apply to the data containedwithin the specified data element types of the database. The functionswhich can be used within a query can include, for example, expressionsusing statistics, sub-queries, filters, mathematical formulas, and thelike, to help the user to locate and/or calculate the specificinformation wanted from the database. Once located and/or calculated,the results of a query can be displayed to the user with variousvisualization techniques and objects such as list boxes of a userinterface illustrated in FIG. 6 .

The graphical objects (or visual representations) can be substantiallyany display or output type including graphs, charts, trees,multi-dimensional depictions, images (computer-generated or digitalcaptures), video/audio displays describing the data, hybridpresentations where output is segmented into multiple display areashaving different data analysis in each area and so forth. A user canselect one or more default visual representations; however, a subsequentvisual representation can be generated on the basis of further analysisand subsequent dynamic selection of the most suitable form for the data.

In an aspect, a user can select a data point and a visualizationcomponent can instantaneously filter and re-aggregate other fields andcorresponding visual representations based on the user's selection. Inan aspect, the filtering and re-aggregation can be completed withoutquerying a database. In an aspect, a visual representation can bepresented to a user with color schemes applied meaningfully. Forexample, a user selection can be highlighted in green, datasets relatedto the selection can be highlighted in white, and unrelated data can behighlighted in gray. A meaningful application of a color scheme providesan intuitive navigation interface in the state space.

The result of a standard query can be a smaller subset of the datawithin the database, or a result set, which is comprised of the records,and more specifically, the data element types and data element valueswithin those records, along with any calculated functions, that matchthe specified query. For example, as indicated in FIG. 6 , the dataelement value “Nisse” can be specified as a query or filtering criteriaas indicated by a frame in the “Client” header row. In some aspects, theselected element can be highlighted in green. By specifically selecting“Nisse,” other data element values in this row are excluded as shown bygray areas. Further, “Year” “1999” and “Month” “January” are selected ina similar way.

Optionally, in this application, external processing can also berequested by ticking “External” in the user interface of FIG. 6 . Dataas shown in FIG. 7 can be exchanged with an External engine 64 throughthe interface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM (Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function (“SUM (ExtFunc(Price*Number))”) can beevaluated. Data sent out are (Nisse, 1999, January, {19.5, null}). Inthis case the external engine 64 can process data in accordance with theformula

if (x==null)  y=0.5 else  y=xas shown in in FIG. 7 . The result input through the interface 66 willbe (19.5, 0.5) as reflected in the graphical presentation in FIG. 6 .

In a further aspect, external processing can also be optionallyrequested by ticking “External” in a box as shown in FIG. 8 . Data asshown in FIG. 9 can be exchanged with an external engine 64 through theInterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM(Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function:

-   -   SUM (ExtFunc(Price*Number))        can be evaluated. Data sent out are (Nisse, 1999, January,        {19.5, null}). In this case, the external engine 64 will process        data in accordance with Function (1) as shown below and in FIG.        9 . The result input through the Interface 66 will be (61.5) as        reflected in the graphical presentation in FIG. 8 .

y=ExtAggr(x[ ])  for (x in x[ ])   if (x==null)    y=y + 42   else   y=y+x  Function (1)

A further optional embodiment is shown in FIG. 10 and FIG. 11 . The samebasic data as in previous examples apply. A user selects “Pekka,”“1999,” “January,” and “External.” By selecting “External,” alreadydetermined and associated results are coupled to the external engine 64.Feedback data from the external engine 64 based on an externalcomputation, ExtQualification(Sum(Price*Number)), as shown in FIG. 13will be the information “MVG.” This information can be fed back to thelogical inference engine 18. The information can also be fed back to thegraphical objects of FIG. 10 and as a result a qualification table 68will highlight “MVG” (illustrated with a frame in FIG. 10 ). Othervalues (U, G, and VG) are shown in gray areas. The result input throughthe Interface 66 will be Soap with a value of 75 as reflected in thegraphical presentation (bar chart) of FIG. 10 . FIG. 11 is a schematicrepresentation of data exchanged with an external engine based onselections in FIG. 10 . FIG. 12 is a table showing results fromcomputations based on different selections in the presentation of FIG.10 .

Should a user instead select “Gullan,” “1999,” “January,” and“External,” the feedback signal would include “VG” based on the contentshown in qualification table 68. The computations actually performed inthe external engine 62 are not shown or indicated, since they are notrelevant to the inference engine.

In FIG. 13 a user has selected “G” as depicted by 70 in thequalification table 68. As a result information fed back from theexternal engine 64 to the external engine 62 and further to theinference engine 18 the following information will be highlighted:“Nisse,” “1999,” and “January” as shown in FIG. 13 . Furthermore, theresult produced will be Soap 37.5 as reflected in the graphicalpresentation (bar chart) of FIG. 13 .

A result of the various methods and systems disclosed herein is abusiness analytic solution. The business analytic solution operates onthe data stored and/or generated (e.g., hypercube/multidimensional cube,various indexes, etc. . . . ) by the disclosed methods and systems.Users of the business analytic solution can query the data to obtaininsight into the data. The query can be made, for example, by specifyingdata element types and data element values of interest and by furtherspecifying any functions to apply to the data contained within thespecified data element types of the database. The functions which can beused within a query can include, for example, expressions usingstatistics, sub-queries, filters, mathematical formulas, and the like,to help a user to locate and/or calculate the specific informationwanted from the database. Once located and/or calculated, the results ofa query can be displayed to the user with various visualizationtechniques and objects such as list boxes or various charts of a userinterface. In another aspect, a result of the query can be displayed notonly as visualizations but in the form of natural language, providingthe user an insight overview across data sources and/or data tables.

Provided herein is a “smart” business analytic solution. For example,the business analytic solution can make reasonable defaults at varioussteps of an analysis, from data preparation, to building the data model,and preparing visual and/or text analyses. In an aspect, the businessanalytic solution can guide users to make sensible choices in order toquickly get to both expected answers and new answers (e.g., newinsights). The business analytic solution enables a user to find unknowninsights from the data and presents it to the user with the use of theprecedent-based system disclosed.

Domain experts, such as data architects, or visualization experts, aresources to provide rules (e.g., defaults and guidelines, usually in theform of generic best practices) for data analysis. Similarly, specificprecedents that are established by users or a community of users whoactually use the data are also sources of rules (e.g., defaults andguidelines) for data analysis. The disclosed methods and systems presentan optimized technique to capture and represent such rules. Given theheuristic nature of such rules, the disclosed methods and systems canutilize precedents to capture both types of rules (e.g., domain expertrules and user rules). Such precedents can then be utilized in a systemthat, given a specific context, can locate applicable precedents (forexample, by similarity and/or generalization) and use those precedentsto enable smart data analysis behavior.

FIG. 14 provided is a method 1400 for undetermined query analytics. Atstep 1402, undetermined query information (e.g., an imprecise query, anundefined query, an incomplete query, a partially expressed query, aportioned query, etc.) may be received. For example, a computing device(e.g., a computer, a cloud-based device, a server, a smart device, etc.)may receive the query information via a user interface and/or from auser device (e.g., a client device, a smart device, a computing device,a network device, etc.). For example, the computing device may receivequery information such as each of the following undeterminedbusiness-related queries:

-   -   Query 1: Sales by product where sales>2000    -   Query 2: Products with sales>2000    -   Query 3: Number of products with sales>2000.

At step 1404, one or more data constraints and one or more data analysismodels may be determined based on the undetermined query information.The one or more data constraints may include one or more temporal dataconstraints, logical data constraints, and data-type constraints.determining the one or more data constraints may include mapping one ormore textual elements of the undetermined query information to the oneor more data constraints. The one or more data models may include one ormore of: a data chart, a data table, a data graph, a data map, and/orkey performance indicators (KPIs).

For example, Query 1, Query 2, and Query 3, each include similar (e.g.,conceptually similar, etc.) items (e.g., compositional elements,predicates, etc.), such as “sales,” “products,” “>2000,” and/or thelike. The computing device may, for example, use natural languageparsing and/or metadata analysis to determine the items and/or anyconstraints of the query, such as a default analysis period, a requireddata/element selection, and/or the like.

The one or more data analysis models may be determined based on thecomposition (e.g., dimensions, measures, etc.) of the one or more dataanalysis models. For example, how each input item and/or data constraintof a undetermined query fits a data analysis model based on the dataanalysis model's capacity and/or projectability of an item (e.g.,whether it has any condition, whether the condition results on one ormultiple values, etc.) may be used to determine the one or more dataanalysis models. For example, input items and/or data constraintsassociated with a rank and/or ranking may be best fitted to a bar chartand/or related data analysis model, input items and/or data constraintsassociated with values may be best fitted to a table, input items and/orcomputational elements associated with facts may be best fitted to a KPIand/or related data analysis model.

At step 1406, an aggregated data set may be determined. An aggregateddata set may be determined based on the one or more data constraints.Determining the aggregated dataset may include applying the one or moreconstraints to an aggregation function. For example, The computingdevice may determine/perform a different set analysis (e.g., executionof an aggregation function, etc.) for Query 1, Query 2, Query 3, and/orany other undetermined query based on, for example, an order/arrangementof items (and/or constraints) of the query.

For example, for Query 1, Query 2, and/or Query 3, all of thecombinations of dimensions and values necessary to perform thecalculation may include any constraint (and/or conditional element) ofthe query. The constraint (and/or conditional element) of the query maybe applied to each measure by injecting a set modifier into acorresponding aggregation function. For example, the measure for a rankanalysis may be as follows:

=sum({<Set1, Set2>} Sales),

where:

Set1=[Product]={′=Sum({<[QuartersAgo]={0}>} Sales>2000)′}; andSet12=[QuartersAgo]={0}.

The aggregation of Sales is further modified by a set of products whosesales are greater than 2000 (hence the second inner self-aggregation).For an analysis with no Measure (e.g., only dimensions, etc.),essentially the same pattern may be used. However, constraints (and/orconditional elements) are applied to instances of the projectingdimension(s). For example, for Query 2 (Products with sales>2000), theexpression for Product may be as follows:

=Aggr(if (sum({<QuartersAgo]={0}>} Sales)>2000 Only(Product)), Product).

Analysis with no dimension may require, for most cases, the measureexpression to be adjusted by using other constraints (and/or conditionalelements) as simple attribute constraints. For example, for anundetermined query such as “Sales where costs>1000,” the condition onCost may be applied at leaf level (no aggregation). A set expression maybe as follows:

=Sum({<Cost={“>1000”}>} Sales)

However, an exception may occur. The exception is when an explicitdimension, such as “Product” from Query 3 (Number of products withsales>2000), may be found. This happens only for aggregation ofdistinct-count. If so, the measure constraints may be properlyaggregated and applied, for example, as follows:

=count(distinct{<Product={′=Sum({<({<[QuartersAgo]={0}>} Sales)>2000′},({<[QuartersAgo]={0}>} Product).

At step 1408, an optimal data analysis model may be determined. Theoptimal data analysis model may be determined based on the aggregateddataset produced by an aggregation function and the one or more dataanalysis models. For example, the computing device may determine anoptimal data analysis model from one or more data analysis modelsdetermined from an undetermined query that best fits each input itemand/or constraint of the query. For example, determining the optimaldata analysis model may include determining, for each of the one or moredata analysis models, an amount of correspondence between a plurality ofportions of data of the aggregated dataset and a plurality of elementsof the data analysis model, determining, based on the amount ofcorrespondence between the plurality of portions of data of theaggregated dataset and the plurality of elements of each the one or moredata analysis models, a data analysis model of the one or more dataanalysis models associated with a highest amount of correspondence, anddetermining that the data analysis model associated with the highestamount of correspondence is the optimal data analysis model.

The method 1400 may further include determining that the optimal dataanalysis model includes elements of the plurality of elements that donot correspond to one or more portions of the plurality of portions ofdata of the aggregated dataset, and determining, based on the one ormore data constraints, another aggregated dataset. The anotheraggregated dataset may include one or more portions of data thatcorrespond to one or more elements of the plurality of elements of theoptimal data analysis model. The aggregated dataset may be based on datafrom a source and the another aggregated dataset may be based on datafrom a different source. For example, the source associated with theaggregated dataset may include an in-memory data store, and thedifferent source may include a public domain (e.g. Internet, a universalapplication, etc.). Determining the another aggregated dataset mayinclude applying the one or more constraints to the aggregationfunction.

At step 1410, the optimal data analysis model may be output. Thecomputing device may cause the output of the optimal data analysismodel. For example, the optimal data analysis model may be displayed viaa user interface and/or the like. The optimal data analysis model mayindicate at least a portion of the aggregated dataset. For example, theoptimal data analysis model may include data determined based on theaggregating function.

In an exemplary aspect, the methods and systems can be implemented on acomputer 1501 as illustrated in FIG. 15 and described below. Similarly,the methods and systems disclosed can utilize one or more computers toperform one or more functions in one or more locations. FIG. 15 is ablock diagram illustrating an exemplary operating environment forperforming the disclosed methods. This exemplary operating environmentis only an example of an operating environment and is not intended tosuggest any limitation as to the scope of use or functionality ofoperating environment architecture. Neither should the operatingenvironment be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set-top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 1501. The components of thecomputer 1501 can comprise, but are not limited to, one or moreprocessors 1503, a system memory 1512, and a system bus 1513 thatcouples various system components including the one or more processors1503 to the system memory 1512. The system can utilize parallelcomputing.

The system bus 1513 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, or local bus using any ofa variety of bus architectures. The bus 1513, and all buses specified inthis description can also be implemented over a wired or wirelessnetwork connection and each of the subsystems, including the one or moreprocessors 1503, a mass storage device 1504, an operating system 1505,software 1506, data 1507, a network adapter 1508, the system memory1512, an Input/Output Interface 1510, a display adapter 1509, a displaydevice 1511, and a human-machine interface 1502, can be contained withinone or more remote computing devices 1514 a,b,c at physically separatelocations, connected through buses of this form, in effect implementinga fully distributed system.

The computer 1501 typically comprises a variety of computer-readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 1501 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 1512 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 1512 typically contains data such as the data 1507 and/orprogram modules such as the operating system 1505 and the software 1506that are immediately accessible to and/or are presently operated on bythe one or more processors 1503.

In another aspect, the computer 1501 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 15 illustrates the mass storage device 1504which can provide non-volatile storage of computer code, computerreadable instructions, data structures, program modules, and other datafor the computer 1501. For example and not meant to be limiting, themass storage device 1504 can be a hard disk, a removable magnetic disk,a removable optical disk, magnetic cassettes or other magnetic storagedevices, flash memory cards, CD-ROM, digital versatile disks (DVD) orother optical storage, random access memories (RAM), read only memories(ROM), electrically erasable programmable read-only memory (EEPROM), andthe like.

Optionally, any number of program modules can be stored on the massstorage device 1504, including by way of example, the operating system1505 and the software 1506. Each of the operating system 1505 and thesoftware 1506 (or some combination thereof) can comprise elements of theprogramming and the software 1506. The data 1507 can also be stored onthe mass storage device 1504. The data 1507 can be stored in any of oneor more databases known in the art. Examples of such databases comprise,DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL,PostgreSQL, and the like. The databases can be centralized ordistributed across multiple systems.

In an aspect, the software 1506 can comprise one or more of a scriptengine, a logical inference engine, a calculation engine, an extensionengine, and/or a rendering engine. In an aspect, the software 1506 cancomprise an external engine and/or an interface to the external engine.

In another aspect, the user can enter commands and information into thecomputer 1501 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the one or more processors 1503via the human-machine interface 1502 that is coupled to the system bus1513, but can be connected by other interface and bus structures, suchas a parallel port, game port, an IEEE 1394 Port (also known as aFirewire port), a serial port, or a universal serial bus (USB).

In yet another aspect, the display device 1511 can also be connected tothe system bus 1513 via an interface, such as the display adapter 1509.It is contemplated that the computer 1501 can have more than one displayadapter 1509 and the computer 1501 can have more than one display device1511. For example, the display device 1511 can be a monitor, an LCD(Liquid Crystal Display), or a projector. In addition to the displaydevice 1511, other output peripheral devices can comprise componentssuch as speakers (not shown) and a printer (not shown) which can beconnected to the computer 1501 via the Input/Output Interface 1510. Anystep and/or result of the methods can be output in any form to an outputdevice. Such output can be any form of visual representation, including,but not limited to, textual, graphical, animation, audio, tactile, andthe like. The display device 1511 and computer 1501 can be part of onedevice, or separate devices.

The computer 1501 can operate in a networked environment using logicalconnections to one or more remote computing devices 1514 a,b,c. By wayof example, a remote computing device can be a personal computer,portable computer, smartphone, a server, a router, a network computer, apeer device or other common network node, and so on. Logical connectionsbetween the computer 1501 and a remote computing device 1514 a,b,c canbe made via a network 1515, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be throughthe network adapter 1508. The network adapter 1508 can be implemented inboth wired and wireless environments. In an aspect, one or more of theremote computing devices 1514 a,b,c can comprise an external engineand/or an interface to the external engine.

For purposes of illustration, application programs and other executableprogram components such as the operating system 1505 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 1501, and are executed by the one or moreprocessors 1503 of the computer. An implementation of the software 1506can be stored on or transmitted across some form of computer-readablemedia. Any of the disclosed methods can be performed by computerreadable instructions embodied on computer-readable media.Computer-readable media can be any available media that can be accessedby a computer. By way of example and not meant to be limiting,computer-readable media can comprise “computer storage media” and“communications media.” “Computer storage media” comprise volatile andnon-volatile, removable and non-removable media implemented in anymethods or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. Exemplary computer storage media comprises, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case-basedreasoning, Bayesian networks, behavior-based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat an order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

What is claimed:
 1. A method comprising: determining, based onundetermined query information, one or more data constraints and one ormore data analysis models; determining, based on the one or more dataconstraints, an aggregated dataset; determining, based on an amount ofcorrespondence between a plurality of portions of data of the aggregateddataset and a plurality of elements of each of the one or more dataanalysis models, a data analysis model of the one or more data analysismodels associated with a highest amount of correspondence; and causingan output of the data analysis model associated with the highest amountof correspondence.
 2. The method of claim 1, wherein the undeterminedquery information comprises one or more of imprecise query information,undefined query information, incomplete query information, partiallyexpressed query information, or portioned query information.
 3. Themethod of claim 1, wherein the one or more data constraints comprise oneor more temporal data constraints, logical data constraints, ordata-type constraints.
 4. The method of claim 1, wherein determining theone or more data constraints comprises mapping one or more textualelements of the undetermined query information to the one or more dataconstraints.
 5. The method of claim 1, wherein the one or more dataanalysis models comprise one or more of: a data chart, a data table, adata graph, a data map, or key performance indicators (KPIs).
 6. Themethod of claim 1, wherein determining the aggregated dataset comprisesapplying the one or more data constraints to an aggregation function. 7.The method of claim 1, further comprising: determining that the dataanalysis model associated with the highest amount of correspondencecomprises elements of the plurality of elements that do not correspondto one or more portions of the plurality of portions of data of theaggregated dataset.
 8. The method of claim 7, further comprising:determining, based on the one or more data constraints, anotheraggregated dataset, wherein the another aggregated dataset comprises oneor more portions of data that correspond to one or more elements of theplurality of elements of the optimal data analysis model, and whereinthe aggregated dataset is based on data from a first source and theanother aggregated dataset is based on data from a source that differsfrom the first source.
 9. The method of claim 8, wherein the firstsource comprises an in-memory data store, and wherein the source thatdiffers from the first source comprises one or more of an externalengine or a public domain.
 10. The method of claim 8, whereindetermining the another aggregated dataset comprises applying the one ormore data constraints to an aggregation function.
 11. A non-transitorycomputer-readable medium storing processor-executable instructions that,when executed by at least one processor, cause the at least oneprocessor to: determine, based on undetermined query information, one ormore data constraints and one or more data analysis models; determine,based on the one or more data constraints, an aggregated dataset;determine, based on an amount of correspondence between a plurality ofportions of data of the aggregated dataset and a plurality of elementsof each of the one or more data analysis models, a data analysis modelof the one or more data analysis models associated with a highest amountof correspondence; and cause an output of the data analysis modelassociated with the highest amount of correspondence.
 12. Thenon-transitory computer-readable medium of claim 11, wherein theundetermined query information comprises one or more of imprecise queryinformation, undefined query information, incomplete query information,partially expressed query information, or portioned query information.13. The non-transitory computer-readable medium of claim 11, wherein theone or more data constraints comprise one or more temporal dataconstraints, logical data constraints, or data-type constraints.
 14. Thenon-transitory computer-readable medium of claim 11, wherein theprocessor-executable instructions that cause the at least one processorto determine the one or more data constraints further cause the at leastone processor to map one or more textual elements of the undeterminedquery information to the one or more data constraints.
 15. Thenon-transitory computer-readable medium of claim 11, wherein the one ormore data analysis models comprise one or more of: a data chart, a datatable, a data graph, a data map, or key performance indicators (KPIs).16. The non-transitory computer-readable medium of claim 11, wherein theprocessor-executable instructions that cause the at least one processorto determine the aggregated dataset further cause the at least oneprocessor to apply the one or more data constraints to an aggregationfunction.
 17. The non-transitory computer-readable medium of claim 11,wherein the processor-executable instructions further cause the at leastone processor to: determine that the data analysis model associated withthe highest amount of correspondence comprises elements of the pluralityof elements that do not correspond to one or more portions of theplurality of portions of data of the aggregated dataset.
 18. Thenon-transitory computer-readable medium of claim 17, wherein theprocessor-executable instructions further cause the at least oneprocessor to: determine, based on the one or more data constraints,another aggregated dataset, wherein the another aggregated datasetcomprises one or more portions of data that correspond to one or moreelements of the plurality of elements of the optimal data analysismodel, and wherein the aggregated dataset is based on data from a firstsource and the another aggregated dataset is based on data from a sourcethat differs from the first source.
 19. The non-transitorycomputer-readable medium of claim 18, wherein the first source comprisesan in-memory data store, and wherein the source that differs from thefirst source comprises one or more of an external engine or a publicdomain.
 20. The non-transitory computer-readable medium of claim 18,wherein the processor-executable instructions that cause the at leastone processor to determine the another aggregated dataset further causethe at least one processor to apply the one or more data constraints toan aggregation function.